Risk factors of road accident severity and the development of a new system for prevention: New insights from China

•Factors affecting accident severity were investigated.•Quantile regression and extreme value theory approach are employed.•The factors affecting accident severity vary among quantiles.•The gender is an atypical accident factor, which significant only for the higher quantiles.•A new prevention frame...

Full description

Saved in:
Bibliographic Details
Published in:Accident analysis and prevention 2020-03, Vol.136, p.105411-105411, Article 105411
Main Authors: Benlagha, Noureddine, Charfeddine, Lanouar
Format: Article
Language:English
Subjects:
Accident severity
Accidents, Traffic - classification
Accidents, Traffic - mortality
Accidents, Traffic - prevention & control
China
China - epidemiology
Extreme value theory
Fatal accidents
Female
Humans
Male
Prevention strategy
Quantile regression
Risk Factors
Risk-Taking
Sex Factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Factors affecting accident severity were investigated.•Quantile regression and extreme value theory approach are employed.•The factors affecting accident severity vary among quantiles.•The gender is an atypical accident factor, which significant only for the higher quantiles.•A new prevention framework to improve road safety and prevention is proposed. Road accident fatalities and accident severity costs have become top priorities and concerns for Chinese policymakers. Understanding the principal factors that explain accident severity is considered to be the first step towards the adequate design of an accident prevention strategy. In this paper, we examine the contribution of various types of factors (vehicle, driver and others) in explaining accident severity in China. Unlike previous studies, the analysis gives a particular focus on fatal accidents. Using a large sample of 405,177 observations for 4-wheeled vehicles in the year 2017 and various statistical and econometrics approaches (e.g., OLS, quantile regression and extreme value theory), the results show that the factors explaining the severity of accidents differs significantly between normal and extreme severity accidents, e.g. across quantiles. Interestingly, we find that the gender factor is only significant for fatal accidents. In particular, the analysis shows that male drivers have an increased likelihood of extreme risk taking. On the basis of these empirical findings, a new ratemaking approach that aims to improve road safety and prevention is discussed and proposed.
ISSN:0001-4575
1879-2057
DOI:10.1016/j.aap.2019.105411